Yolov5m Football

🚀 Yolov5m-Football Object Detection Model

This is a YOLOv5-based object detection model, specifically trained for football-related object detection tasks. It provides high-precision detection capabilities and can be easily integrated into various applications.

🚀 Quick Start

Installation

To use this model, you first need to install the yolov5 library:

pip install -U yolov5

Usage Examples

Basic Usage

Load the model and perform a simple prediction:

import yolov5

# load model
model = yolov5.load('keremberke/yolov5m-football')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

Advanced Usage

Fine-tune the model on your custom dataset:

yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-football --epochs 10

📚 Documentation

Model Information

Performance Metrics

On the keremberke/football-object-detection validation dataset, the model achieved a mAP@0.5 of 0.7405493668158392.

More Models

More YOLOv5 models are available at awesome-yolov5-models.